Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.
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Okekunle Akinkunmi Paul, Asowata Osahon Jeffery, Fakunle Adekunle Gregory, Omoleye Olasubomi Jimmy, Olaiya Muideen Tunbosun, Kumuthini Judit, Morsy Moustafa I, Caiazzo Elisabetta, Kinengyere Alison Annet, Ajuwon Grace, Nsoesie Elaine O, Olowoyo Paul, Kengne Andre Pascal, Michael Obaro S, Sarfo Fred Stephen, Odedina Folakemi T, Fatumo Segun, Adeoye Abiodun M, Adebiyi Akindele Olupelumi, Akinyemi Rufus Olusola, Agyemang Charles, Baichoo Shakuntala, Ovbiagele Bruce, Maffia Pasquale, Olopade Olufunmilayo, Aribisala Benjamin, Akpa Onoja Matthew, Hamdi Yosr, Owolabi Mayowa Ojo
Аннотация
METHODS: We undertake a systematic review and gap analysis, as registered in PROSPERO (CRD42023406237).
RESULTS: Our findings suggest several data science methods have been used in research across the four leading NCDs in Africa. However, limited information exists on their application to improve disease surveillance, risk factor identification and characterization, prevention, treatment, drug discovery and rehabilitation. Machine learning outperforms traditional statistical methods in improving risk stratification in most studies (80.8%) designed for the prevention and control of NCDs. Notwithstanding, most (76.0%) data science techniques for NCDs prevention and control remain in the exploratory research phase, with limited clinical or public health application and minimal impact on the African population. There are critical gaps along the continuum of data generation, data quality, method development, and validation, which may be attributed to inadequate funding, capacity development, policy shortcomings, and infrastructure deficits. Considerable gaps exist in intra-African collaboration, data sharing, and replication, which hinder the cross-cultural replication and applicability of data science methods for NCDs prevention and control in Africa.
CONCLUSIONS: Multi-sectoral interventions that promote interdisciplinary capacity building, investment, and knowledge linkages, taking into account indigenous epistemologies, are needed to harness the enormous potential of data science to accelerate the prevention and control of NCDs in Africa.